Modern data architectures for Machine learning
From this section onwards, we will cover some of the emergent data architectures, challenges that gave rise to architectures of this implementation architecture, some relevant technology stacks, and use cases where these architectures apply (as relevant) in detail.
Semantic data architecture
Some of the facts covered in the emerging perspectives in the previous section give rise to the following core architecture drivers to build semantic data model driven data lakes that seamlessly integrate a larger data scope, which is analytics ready. The future of analytics is semantified. The goal here is to create a large-scale, flexible, standards-driven ETL architecture framework that models with the help of tools and other architecture assets to enable the following:
- Enabling a common data architecture that can be a standard architecture.
- Dovetailing into the Ontology-driven data architecture and data lakes of the future (it is important to tie this architecture...
